6 Steps to Perfect Qual

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Step 1: Qualitative research design

With an optimized research design, a small consultancy can really punch above its weight.

This first official L&E trust guide shares the path to an optimized research design to start you out on your qualitative journey.

This paper will guide you through qualitative research design best practices to guarantee actionable insight for your client’s needs.

• How to define the client problem and structure the “big question”
• The right methodology for the right solution
• How to create moderator guidelines and define sample sizes
• Choosing a facility location
• Technology that is changing the nature of qualitative research
• The expertise behind Research Design Engineers

To continue reading, download our latest white paper by clicking here (no form to fill out).

AI and Automation – More Time for the Good Stuff.

AI and Automation.  These are terms that were first associated with big data, then with structured data, and now with small unstructured data (read “qualitative”).  The capabilities of AI and automation are filtering into most aspects of the qualitative process.  And the result is qualitative research that is often cheaper, faster, and – recognizing that this word is subjective – better.  But more importantly, it opens the analyst’s time for the good stuff – solving the client problem.

Here are a few ways our qualitative life is being changed by automation and AI.

• Sampling – sample providers are using API’s to integrate with qualitative platforms to enable fast, efficient, and cheap access to consumers for a variety of qualitative approaches: communities, IDI’s, online groups and emerging hybrid approaches.
• Data collection – Online platforms for qualitative and hybrid approaches  allow for quick turn qualitative research unimaginable a few years ago. Need to conduct 12 groups globally in a day? Done.  Need to test concepts with 1000 people and get qualitative feedback in 24 hours? Done.  Need to conduct “micro-communities” for a week on new positioning? Done.
• Moderation – the emergence of talent marketplaces now gives buyers the ability to become research DJ’s; to mix the right individuals with the right skillsets globally with the right technology to meet the business need in real time and all online. Qualitative research is entering the “Democratization Age”, where technology drives the human elements as much as it does the data collection process.
• Analysis – advances in AI, text analytics and data visualization tools now allow for the fast (within seconds, literally) categorization, clustering, thematic discovery, emotional analysis and even voice or facial analysis of responses as part of the tool kit. This makes the analysis of transcription, video, image and voice data as easy as running basic descriptive statistics in quantitative data.

It’s important to note that the effectiveness of all these tools are dependent on the right people, talking about the right things, in the right way.  Recruiting, study structure, and moderation are still critical elements.  AI and automation are still only as good as the thoughtful humans directing it. The advancement in these tools are drastically reducing the human time and effort it takes to gather and analyze qualitative information while improving the types of information and volume of information that can be analyzed.

This leaves the analyst with more information to work with and more time to focus on the implications of the insights to address the business issue.  Isn’t that what we really need – more time for the good stuff?

Writing a high-quality screener is critical to getting the right respondents for your qualitative research project. Since screeners are actually short questionnaires, why not apply the same skills and techniques to developing your screeners? On May 12, 2017 L&E hosted a webinar with David F. Harris, author of, The Complete Guide to Writing Questionnaires: How to Get Better Information for Better Decisions. David provided detailed examples of how to improve your screeners to get better respondents for your qualitative research.

Webinar Presentation

To download a PDF copy of David’s presentation from the webinar, click here.

Webinar Recording

About David F. Harris

David F. Harris conducts qualitative and quantitative research for companies in a variety of industries. As founder of Insight & Measurement, he also conducts training and consulting on questionnaire design and screener development. He is author of, The Complete Guide to Writing Questionnaires: How to Get Better Information for Better Decisions. He received his B.A. from Reed College, in Portland, Oregon, and his M.A. in Quantitative Psychology from the L. L. Thurstone Psychometric Laboratory at the University of North Carolina at Chapel Hill.

Prefrontal Cortex, Limbic System, System 1, Behavioral Economics, Emotional Quotient – all phrases that didn’t matter in the market research industry until recently (the definition of recently being up for debate). The point is, market researchers have learned that emotions play a critical role in consumer behavior and satisfaction.

Knowing that emotions play a role and knowing which emotions play what role are two very different things. While no one has all the answers to this problem (and all the answers may not even exist), here are some issues to consider when you take on the task of understanding emotions in a business context.

• Segmentation: It’s not news that people are different from one another. But particularly with emotional measurement, the emotions that people feel about a category, product, or brand are often related to their degree of engagement. For example, heavy users of Apple have a different and deeper emotional connection to the products and brand than moderate users of Apple. In addition to engagement, the emotional nature of the individual person can play a role in the connection to the brand. There are several ‘personality traits’ type models that can help structure the understanding that comes through qualitative research.
• Context: When delving into the emotions elicited by a category, product, or brand – context can be incredibly important depending on the nature of the product and the situations in which the product is used. Some products are personal by nature in that there is not much social interaction related to the product. Cleaning supplies are a reasonable example in that they are generally used in a private setting (Saturday morning around the house) and not part of a large social effort. In contrast, birthday cakes are almost always used in a social setting – be it large or small. The emotions elicited by either can be generally viewed as the emotions in any situation where these products are used. But let’s take the example of coffee. Coffee is sometimes used in personal settings and sometimes in social settings. The emotions elicited in each can be very different from each other with the exact same product.

• Conscious: Many of the System 1 approaches would have you believe that all emotions are nonconscious or the decisions made from these emotions are all nonconscious. While this may be true for some people, most can articulate real emotions with a modest degree of depth and accuracy. Happy vs. Sad, Scared vs. Comforted, etc. In situations where nuance is not warranted, in depth interviews (and a good tool kit from a moderator) may be sufficient to uncover the emotions and emotional drivers for a project.
• Nonconscious – Implicit vs. Biometric Measures: In those cases where nuance is required or the respondent may be either unwilling or unable to understand or articulate, implicit association and biometric feedback are the two categories of tools that offer the most insight. A distinction is made here because definitions of these two words are wavering – Implicit is any tool that delves into nonconscious emotions but does not measure some component of the body in doing so. For (at least our definition of) implicit, the tool kit is generally an implicit association test or metaphor elicitation. There are several good tools and techniques that fall in or near these definitions. Biometric feedback has made important advance in quality and cost over the past few years and is therefore being used by more and more clients. The most common of these tools are eye-tracking and facial coding. Others that are reasonably available for qualitative research include Galvanic Skin Response, EEG, fMRI, and heart rate monitoring. While these are still early in their evolution and there is much to learn, these tools can help researchers understand emotions and emotional triggers.

Emotional measurement is difficult. However, with guidance and tools, it is easier than it ever has been, and these tools make a useful addition to the qualitative researcher’s toolkit to help deliver deeper insights that deliver greater business value.